R Packages for Risk Assessment
Régis Pouillot and Marie Laure Delignette-Muller
Two R1 packages specifically developed to help risk assessors in their projects are now available.
The first package, "fitdistrplus", gathers graphical and statistical tools for choosing and fitting a parametric univariate distribution to a given dataset. The data may be continuous or discrete. A major interest of this package lays in its ability to deal with right-, left- or interval-censored continuous data as is frequently obtained when analytical methods have a limit of detection or a limit of quantification. Various censoring thresholds may be present within a dataset. Bootstrap procedures then allow the assessor to evaluate and model the uncertainty around the distribution parameters and to transfer this information into a quantitative risk assessment model.
The second package, "mc2d", helps to build and study two dimensional (or second-order) Monte-Carlo simulations in which the estimation of variability and uncertainty in the risk estimates is separated. This package easily allows the transfer of separated variability and uncertainty along a chain of conditional mathematical and probabilistic models.
These packages are mainly suitable for users with an intermediate experience with R.
Downloads
“fitdistrplus” package
- fitdistrplus
- Reference manual
- Vignette (Manual that illustrates its use in the food safety domain)
“mc2d” package
- mc2d
- Reference manual
- Vignette (Manual that illustrates its use in the food safety domain)
- Case study: Quantitative risk assessment of L. monocytogenes in cold smoked salmon
Peer review publication
R. Pouillot, M.-L. Delignette-Muller. Evaluating variability and uncertainty separately in microbial quantitative risk assessment using two R packages. International Journal of Food Microbiology, 2010, 142(3): 330-340
For more information please contact: M.-L. Delignette-Muller or R. Pouillot
1. R is an open-source integrated suite of software facilities for data manipulation, calculation, and graphical display, which is extended by a large collection of packages in which up-to-date statistical methods are implemented (R Development Core Team, 2009)

